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Deep learning combined with fluorescence imaging quantifies topical drug uptake in human skin. This method overcomes skin autofluorescence, simplifying pharmacokinetic analysis for drug development.

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Area of Science:

  • Dermatology
  • Pharmacokinetics
  • Biomedical Imaging
  • Artificial Intelligence

Background:

  • Quantifying topical drug pharmacokinetics (PK) in skin is challenging.
  • Traditional methods like mass spectrometry are complex and costly for routine use.
  • Skin autofluorescence interferes with optical methods for fluorescent drug tracking.

Purpose of the Study:

  • To develop a simplified method for visualizing and quantifying fluorescent topical drug uptake in human skin.
  • To overcome limitations posed by skin autofluorescence in PK studies.
  • To enable routine application in topical product development.

Main Methods:

  • Utilized standard epi-fluorescence imaging combined with deep learning (U-Net convolutional neural network).
  • Trained the neural network on a dataset of annotated images.
  • Applied the method to quantify drug uptake from both high and clinical dose regimens.

Main Results:

  • Successfully visualized and quantified fluorescent drug uptake in human skin.
  • Demonstrated accurate measurement of drug concentration despite skin autofluorescence.
  • Quantified uptake from both high-dose and clinically relevant daily dose regimens.

Conclusions:

  • Deep learning and epi-fluorescence imaging offer a powerful approach for dermal PK analysis.
  • This technique simplifies the quantification of topical drug uptake in human skin.
  • The method has potential to streamline routine topical product development and research.